Electronic content providers, such as advertisers and content publishers, have been using various methods to send electronic content to users. Such conventional methods include sending email messages, presenting banner ads on websites, sending text messages, presenting pop-up ads, placing ads in online search results, etc. These content providers have found that users are more likely to view and act on content (e.g., redeem a coupon, click on an ad, etc.) if the content is relevant to them. Therefore, increasingly, content providers have been using content targeting methods based on various attributes of a user. For example, content providers target users based on a user's web browsing history, etc.
Some online content providers, such as advertisers and content publishers, have been sending targeted content to users' electronic devices based on the physical location of the user by analyzing the IP address and other user information (e.g., search history, cookies, etc.) transmitted or retrievable from a user's electronic device. For example, many online advertisers and publishers deliver electronic content (such as ads and multimedia) to users' devices based on IP address-based inferences of each user's general location, i.e., which city or country they are predicted to be in. However, such methods are coarse, in that they do not target content based on information more granular than the general area where the user is located. Such methods are also susceptible to location misdirection based on user tools such as virtual environments or desktops, and IP address detection blocking. As a result, other methods for geographically targeted ads and content have been developed based on more granular levels of targeting. For example, businesses may “push” ads and/or content to users' devices when the user enters and connects to a Wi-Fi and/or Bluetooth network operated by the business. However, such methods are quite limited by the relatively short reach of the wireless network employed. Such methods are unable to reach a sufficiently large local audience that is within a desired area for making a decision to patronize the business.
Other online content providers target users within a predetermined geographic zone so that users within the zone receive the targeted content. For example, some online content providers detect the presence of an electronic device user within a certain radius of a target location, and then send the user an ad. These radius or other geographic based methods of providing electronic content may be arbitrary and may not account for variations within different geographic regions. This may result in the electronic content being provided to too few or to too many users.
These predetermined geographic zones, commonly referred to as geo-fences or geofences, are digital, virtual boundaries having a predetermined size. Electronic content providers, such as national advertisers, often approach geographic targeting by constructing the same size radius geo-fence around each of their properties (e.g., a 2-mile radius around every store). While this is simple for the advertiser, it fails to take into account population density and therefore leads to both false negatives and false positives. For example, a one-mile radius around a store in Manhattan, might include 14 ZIP codes, while a one-mile radius around a store in rural Pennsylvania might only include one ZIP code. The number of users reached by the electronic content would be high in Manhattan but conversion rates would likely be low—while the reach might be far too low in Pennsylvania. Conversion rates are based on the number of users acting on the content or ad. For example, if 10,000 users are provided an electronic coupon for a car wash and 3,000 of those users actually redeem the coupon, the conversion rate may be 0.3.
Therefore, there is a tradeoff between the size of a geo-fence and the conversion of consumer behavior: the larger the geo-fence, the more users will receive the electronic content (e.g. an electronic coupon), but the conversion rate may be low. Conversely, the smaller the geo-fence, the fewer users will receive the electronic content, but there may be a high conversion rate. Advertisers attempt to manually optimize the breadth of the geo-fence and the resulting conversion rate but existing methods are too static and unintelligent to enable this.
Accordingly, a need exists for methods and systems of providing improved dynamic targeting of electronic content based on other factors, for example, the population density of a geographic region to obtain reach equivalency of electronic content.